Aug 25, 2016
excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.
Jan 17, 2017
Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.
автор: Ernie M•
Sep 25, 2017
I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.
Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.
Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.
Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.
автор: Tsz W K•
May 15, 2017
The materials presented are excellent with well prepared skeleton codes for all ML models. Comparing this course to its three preceding ones, this course is more challenging both conceptually and computationally. The slight drawback is that, because of the highly technical nature of the last three weeks' materials, there isn't enough guidance about how one may construct the ML algorithms from scratch, that is, learners with less experience in computing will, more or less, have to accept the sample codes with little confidence about how to (re)write such codes in the first place.
As a result, I believe that learners with more experience in algorithms and data structure (or learners who proceed to learn more about this area) are likely to gain more from this course for at least two reasons: i) they are more comfortable with the complicated ML algorithms; ii) they can improve the algorithms to speed up the estimation time (some advanced techniques are quite computationally expensive, say over 20 minutes).
In general, I have learnt very much from this course and love it.
автор: Eugene K•
Feb 10, 2017
If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.
Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.
автор: Yufeng X•
Jul 09, 2019
It opened the door to more advanced techniques.
Jul 08, 2019
I like the course very much. I learnt so many advance concept and real life implementation.. but slightly disappointed by the quiz question please be specific what you wanted us to answer. looking forward for SVM and deep learning material.
автор: Jafed E•
Jul 06, 2019
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
автор: Christopher M•
Jul 01, 2019
Doesn't go quite as deep into the details as some of the other Machine Learning courses from the University of Washington do. Overall though, the course covers a LOT of ground. and provides exposure to many different topics.
I would have liked to have seen an Optional section on the derivation of some of the math that we were given functions for on the Expectation Maximization section. The models in the hierarchical clustering section take longer to fit than is necessary for a course like this (more than 40 times as long as the instructions say it should take), maybe a larger tolerance for convergence should be specified?
автор: Mohamed A H•
Jun 20, 2019
A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well.
It was an amazing experience!
автор: Aakash S•
Jun 19, 2019
Such a clear explanation of topics of clustering. Without doubt one of the best in business.
автор: Dimitrios Z•
Jun 08, 2019
It has intresting theory but I believe the exercises need to be improvised. Maybe using Jupyter online and guiding the student to write code to solve the problems. In conclusion, I understood the basic theory but mostly that.
автор: Dohyoung C•
Jun 04, 2019
LDA is little bit difficult to understand, but K-mean and Mixture models are easy to understand and quite important for clustering..
автор: YASHKUMAR R T•
May 31, 2019
Awesome course to understand the concept behind Gaussian Mixture model.
автор: Dennis S•
May 19, 2019
Amazing course. The Instructors did an awesome job of preparing and presenting the material.
I think there is no better and more approachable in-depth course out there. Thank you so much!
автор: kripa s•
Apr 30, 2019
One of the best training experience...
автор: Martin B•
Apr 11, 2019
Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.
автор: Akash G•
Mar 11, 2019
Machine Learning: Clustering & Retrieval good and learn easily
автор: Sathiraju E•
Mar 03, 2019
Very nice course. Things are well explained, however some concepts could be expanded more.
автор: Jialie ( Y•
Feb 21, 2019
The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies
автор: Edwin P•
Feb 15, 2019
Excellent, good contribution to the technical and practical knowledge ML
автор: Zhongkai M•
Feb 12, 2019
Great assignments : )
автор: Vikash S N•
Feb 03, 2019
It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!
автор: Srinivas C•
Jan 07, 2019
This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.
автор: Jay K S•
Jan 05, 2019
Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.
автор: KAI N•
Jan 03, 2019
Excellent course with great and reachable explanation
автор: PRAVEEN R U•
Dec 27, 2018
Nice content and well made presentations.